Cargando…

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this pro...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Weizhong, Tang, Ye, Wu, Hongjie, Huang, Hongmei, Fu, Qiming, Qiu, Jing, Li, Haiou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929275/
https://www.ncbi.nlm.nih.gov/pubmed/31874602
http://dx.doi.org/10.1186/s12859-019-3258-7
_version_ 1783482666926997504
author Lu, Weizhong
Tang, Ye
Wu, Hongjie
Huang, Hongmei
Fu, Qiming
Qiu, Jing
Li, Haiou
author_facet Lu, Weizhong
Tang, Ye
Wu, Hongjie
Huang, Hongmei
Fu, Qiming
Qiu, Jing
Li, Haiou
author_sort Lu, Weizhong
collection PubMed
description BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. RESULTS: To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. CONCLUSIONS: Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.
format Online
Article
Text
id pubmed-6929275
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-69292752019-12-30 Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter Lu, Weizhong Tang, Ye Wu, Hongjie Huang, Hongmei Fu, Qiming Qiu, Jing Li, Haiou BMC Bioinformatics Research BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. RESULTS: To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. CONCLUSIONS: Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods. BioMed Central 2019-12-24 /pmc/articles/PMC6929275/ /pubmed/31874602 http://dx.doi.org/10.1186/s12859-019-3258-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lu, Weizhong
Tang, Ye
Wu, Hongjie
Huang, Hongmei
Fu, Qiming
Qiu, Jing
Li, Haiou
Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title_full Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title_fullStr Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title_full_unstemmed Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title_short Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
title_sort predicting rna secondary structure via adaptive deep recurrent neural networks with energy-based filter
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929275/
https://www.ncbi.nlm.nih.gov/pubmed/31874602
http://dx.doi.org/10.1186/s12859-019-3258-7
work_keys_str_mv AT luweizhong predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT tangye predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT wuhongjie predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT huanghongmei predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT fuqiming predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT qiujing predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter
AT lihaiou predictingrnasecondarystructureviaadaptivedeeprecurrentneuralnetworkswithenergybasedfilter